中国农业科技导报2025，Vol.27Issue(3)：104-111,8.DOI:10.13304/j.nykjdb.2023.0640
基于高光谱技术与主成分分析的稻种品种识别研究
Research on Rice Variety Identification Based on Hyperspectral Technology and Principal Component Analysis
摘要
Abstract
In order to rapidly and accurately identificate rice germplasm resources,an efficient identification method was developed based on hyperspectral analysis.Taking 9 rice varieties in the South China rice region as experimental samples,the hyperspectral reflectances of 2 700 seeds were obtained by spectrometer,and principal component analysis(PCA)was used to reduce the dimensionality of the hyperspectral data.To explore the optimal number of principal components in PCA,this paper compared the effect of combining different principal component numbers and discriminant analysis methods(linear discriminant,quadratic discriminant,and Markov distance discriminant)in establishing a rice variety recognition model based on seed hyperspectral data.Principal component analysis on full band data samples was performed,3 variety discrimination models for prediction were established using 2～20 principal components as feature variables and the accuracy of the prediction set as an evaluation indicator,and their effects were compared.The results showed that if taking the cumulative contribution rate≥85%as evaluation criterion,2 principal components were selected,and the accuracy rates of the 3 models prediction sets were 32.14%,38.69%and 33.73%,respectively;when using eigenvalues≥1 as standard,11 principal components were selected,and the accuracy rates of the prediction sets were 68.21%,87.33%,and 83.18%,respectively;considering the accuracy of the model,20 principal components were selected and the accuracy of the prediction set was 84.99%,95.71%,and 95.32%,respectively.The rice hyperspectral variety recognition model established using principal component analysis and discriminant analysis methods was feasible,but the different number of principal components and DA method evaluation standards resulted in significantly different recognition effectiveness.When the number of principal components was same,the quadratic discriminant analysis method had the best recognition effect among 3 discriminant standards.The best combination was 20 principal components+quadratic discriminant analysis method,and the accuracy of the prediction was 95.71%.The research on rice variety recognition based on hyperspectral technology and principal component analysis could quickly identify different rice varieties and had high application value.关键词
稻种/高光谱技术/主成分分析/品种识别Key words
rice seeds/hyperspectral technology/principal component analysis/variety identification分类
农业科学引用本文复制引用
陈林涛,刘兆祥,蓝莹,牟向伟,马旭,王日俊..基于高光谱技术与主成分分析的稻种品种识别研究[J].中国农业科技导报,2025,27(3):104-111,8.基金项目
桂林市重大专项计划项目(20220102-3) （20220102-3）
桂林市重点研发计划项目(20210208-2) （20210208-2）
广西重点研发计划项目(2021AB38023). （2021AB38023）
